Performance Characteristics of Adenoid Cystic Carcinoma of the Salivary Glands in Fine-Needle Aspirates: Results From the College of American Pathologists Nongynecologic Cytology Program
Bibliographic record
Abstract
CONTEXT: Although the cytomorphology of adenoid cystic carcinoma (ACC) has been well described, the accuracy of this diagnosis in fine-needle aspirates (FNAs) of the salivary glands has not been extensively evaluated. OBJECTIVE: To assess participants' responses in the College of American Pathologists (CAP) Nongynecologic Cytology (NGC) Program to determine the accuracy and false-negative rate of ACC cases in salivary gland FNAs. DESIGN: A retrospective review of the CAP NGC Program's cumulative data from 2000-2010 was performed for the general and the specific reference diagnosis categories for ACC in salivary gland FNAs according to preparation and participant types. RESULTS: Of 5156 responses, the overall concordance rates for both the general category of malignancy and the specific category of ACC were 63.6% (3279 of 5156) and 38.6% (1966 of 5088), respectively, with a false-negative rate of 36.4% (1877 of 5156). The most frequent false-negative responses were pleomorphic (1080) and monomorphic (526) adenoma (1614 of 5088, 31.5%), while lymphoma was the most frequent malignant misinterpretation. There was a significant statistical difference in concordance to the reference interpretation between the reader types: 39.9% (1006 of 2521) concordance rate for pathologists compared to 33.8% (503 of 1488) for cytotechnologists. However, there was no significant statistical difference for concordance to the general category or reference interpretation, based on preparation type (Papanicolaou versus modified Giemsa stained). CONCLUSIONS: In this interlaboratory comparison educational program, accurate identification of ACC has shown to be problematic, with ACC representing an important cause of false-negative responses. The most common diagnostic pitfall is distinguishing this entity from pleomorphic and monomorphic adenoma in the benign category and from lymphoma and adenocarcinoma in the malignant one.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".